SoroushVahidi/scidocs huggingface dataset:
Children ’ s Eye Movements during Listening : Developmental Evidence for a Constraint-Based Theory of Sentence Processing
(2004)

Many comprehension studies of grammatical development have focused on the ultimate interpretation that children assign to sentences and phrases, yielding somewhat static snapshots of children's emerging grammatical knowledge. Studies of the dynamic processes underlying children's language comprehension have to date been rare, owing in part to the lack of online sentence processing techniques suitable for use with children. In this chapter, we describe recent work from our research group, which examines the moment-by-moment interpretation decisions of children (age 4 to 6 years) while they listen to spoken sentences. These real-time measures were obtained by recording the children's eye movements as they visually interrogated and manipulated objects in response to spoken instructions. The first of these studies established some striking developmental differences in processing ability, with the youngest children showing an inability to use relevant properties of the referential scene to resolve temporary grammatical ambiguities (Trueswell, Sekerina, Hill, & Logrip, 1999). This finding could be interpreted as support for an early encapsulated syntactic processor that has difficulty using non-syntactic information to revise parsing commitments. However, we will review evidence from a series of follow-up experiments which suggest that this pattern arises from a developing interactive parsing system. Under this account, adult and child sentence comprehension is a " perceptual guessing game " in which multiple statistical cues are used to recover detailed linguistic structure. These cues, which include lexical-distribution evidence, verb semantic biases, and referential scene information, come " online " (become automated) at different points in the course of development. The developmental timing of these effects is related to their differential reliability and ease of detection in the input.
Development and natural history of mood disorders
(2002)

To expand and accelerate research on mood disorders, the National Institute of Mental Health (NIMH) developed a project to formulate a strategic research plan for mood disorder research. One of the areas selected for review concerns the development and natural history of these disorders. The NIMH convened a multidisciplinary Workgroup of scientists to review the field and the NIMH portfolio and to generate specific recommendations. To encourage a balanced and creative set of proposals, experts were included within and outside this area of research, as well as public stakeholders. The Workgroup identified the need for expanded knowledge of mood disorders in children and adolescents, noting important gaps in understanding the onset, course, and recurrence of early-onset unipolar and bipolar disorder. Recommendations included the need for a multidisciplinary research initiative on the pathogenesis of unipolar depression encompassing genetic and environmental risk and protective factors. Specifically, we encourage the NIMH to convene a panel of experts and advocates to review the findings concerning children at high risk for unipolar depression. Joint analyses of existing data sets should examine specific risk factors to refine models of pathogenesis in preparation for the next era of multidisciplinary research. Other priority areas include the need to assess the long-term impact of successful treatment of juvenile depression and known precursors of depression, in particular, childhood anxiety disorders. Expanded knowledge of pediatric-onset bipolar disorder was identified as a particularly pressing issue because of the severity of the disorder, the controversies surrounding its diagnosis and treatment, and the possibility that widespread use of psychotropic medications in vulnerable children may precipitate the condition. The Workgroup recommends that the NIMH establish a collaborative multisite multidisciplinary Network of Research Programs on Pediatric-Onset Bipolar Disorder to achieve a better understanding of its causes, course, treatment, and prevention. The NIMH should develop a capacity-building plan to ensure the availability of trained investigators in the child and adolescent field. Mood disorders are among the most prevalent, recurrent, and disabling of all illnesses. They are often disorders of early onset. Although the NIMH has made important strides in mood disorders research, more data, beginning with at-risk infants, children, and adolescents, are needed concerning the etiology and developmental course of these disorders. A diverse program of multidisciplinary research is recommended to reduce the burden on children and families affected with these conditions.
Clique Graphs and Overlapping Communities
(2010)

It is shown how to construct a clique graph in which properties of cliques of a fixed order in a given graph are represented by vertices in a weighted graph. Various definitions and motivations for these weights are given. The detection of communities or clusters is used to illustrate how a clique graph may be exploited. In particular a benchmark network is shown where clique graphs find the overlapping communities accurately while vertex partition methods fail. PACS numbers: 89.75.Hc, 89.75.Fb, 89.75.-k
Document Image Quality Assessment: A Brief Survey
(2013)

To maintain, control and enhance the quality of document images and minimize the negative impact of degradations on various analysis and processing systems, it is critical to understand the types and sources of degradations and develop reliable methods for estimating the levels of degradations. This paper provides a brief survey of research on the topic of document image quality assessment. We first present a detailed analysis of the types and sources of document degradations. We then review techniques for document image degradation modeling. Finally, we discuss objective measures and subjective experiments that are used to characterize document image quality.
Online Anomaly Detection in Crowd Scenes via Structure Analysis
(2015)

Abnormal behavior detection in crowd scenes is continuously a challenge in the field of computer vision. For tackling this problem, this paper starts from a novel structure modeling of crowd behavior. We first propose an informative structural context descriptor (SCD) for describing the crowd individual, which originally introduces the potential energy function of particle's interforce in solid-state physics to intuitively conduct vision contextual cueing. For computing the crowd SCD variation effectively, we then design a robust multi-object tracker to associate the targets in different frames, which employs the incremental analytical ability of the 3-D discrete cosine transform (DCT). By online spatial-temporal analyzing the SCD variation of the crowd, the abnormality is finally localized. Our contribution mainly lies on three aspects: 1) the new exploration of abnormal detection from structure modeling where the motion difference between individuals is computed by a novel selective histogram of optical flow that makes the proposed method can deal with more kinds of anomalies; 2) the SCD description that can effectively represent the relationship among the individuals; and 3) the 3-D DCT multi-object tracker that can robustly associate the limited number of (instead of all) targets which makes the tracking analysis in high density crowd situation feasible. Experimental results on several publicly available crowd video datasets verify the effectiveness of the proposed method.
BER analysis of optical eU-OFDM transmission over AWGN
(2015)

Enhanced unipolar orthogonal frequency-division multiplexing (eU-OFDM) was recently introduced to improve the power-spectral efficiency trade-off of intensity-modulated direct-detection (IM/DD) optical OFDM transmission schemes. This is accomplished by superimposing unipolar signals over several layers. Bit-error-rate (BER) analysis for each layer however, is eluded by error-propagation from previous layers in the eU-OFDM receiver. This paper presents a general mathematical model for eU-OFDM transmission. From the model, we introduce successive interference cancellation using soft-symbol estimates, which leads to slight performance improvement compared to the original hard-decision counterpart. More importantly, by using soft-symbol estimates we derive analytically tractable expressions that accurately predict the system's performance.
A comprehensive study on security attacks on SSL/TLS protocol
(2016)

Secure Socket Layer (SSL) protocol was introduced in 1994 and was later renamed as transport layer security (TLS) protocol for securing transport layer. SSL/TLS protocol is used for securing communication on the network by ensuring data confidentiality, data integrity and authenticity between the communicating party. Authentication of the communicating party and securing transfer of data is done through certificates, key exchange and cipher suites. Security issues were found during evolutionary development of SSL/TLS protocol. The paper gives a detailed chronological order of attacks of past 22 years on SSL/TLS protocol.
Post-Punching Behavior of Flat Slabs by
(2013)

Reinforced concrete flat slabs are a common structural system for cast-in-place concrete slabs. Failures in punching shear near the column regions are typically governing at ultimate. In case no punching shear or integrity reinforcement is placed, failures in punching develop normally in a brittle manner with almost no warning signs. Furthermore, the residual strength after punching is, in general, significantly lower than the punching load. Thus, punching of a single column of a flat slab overloads adjacent columns and can potentially lead to their failure on punching, thus triggering the progressive collapse of the structure. Over the past decades, several collapses have been reported due to punching shear failures, resulting in human casualties and extensive damage. Other than placing conventional punching shear reinforcement, the deformation capacity and residual strength after punching can also be enhanced by placing integrity reinforcement to avoid progressive collapses of flat slabs. This paper presents the main results of an extensive experimental campaign performed at the Ecole Polytechnique Fédérale de Lausanne (EPFL) on the role of integrity reinforcement by means of 20 slabs with dimensions of 1500 x 1500 x 125 mm (≈5 ft x 5 ft x 5 in.) and various integrity reinforcement layouts. The performance and robustness of the various solutions is investigated to obtain physical explanations and a consistent design model for the load-carrying mechanisms and strength after punching failures. INTRODUCTION Over the past decades, several collapses due to punching shear failure have been reported in Europe and America,
User Interface Menu Design Performance and User Preferences: A Review and Ways Forward
(2016)

This review paper is about menus on web pages and applications and their positioning on the user screen. The paper aims to provide the reader with a succinct summary of the major research in this area along with an easy to read tabulation of the most important studies. Furthermore, the paper concludes with some suggestions for future research regarding how menus and their positioning on the screen could be improved. The two principal suggestions concern trying to use more qualitative methods for investigating the issues and to develop in the future more universally designed menus. Keywords—Menus; navigation of interfaces; universal design;
PEGASUS: A Policy Search Method for Large MDPs and POMDPs
(2000)

We propose a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable Markov decision process (POMDP), given a model. Our approach is based on the following observation: Any (PO)MDP can be transformed into an “equivalent” POMDP in which all state transitions (given the current state and action) are deterministic. This reduces the general problem of policy search to one in which we need only consider POMDPs with deterministic transitions. We give a natural way of estimating the value of all policies in these transformed POMDPs. Policy search is then simply performed by searching for a policy with high estimated value. We also establish conditions under which our value estimates will be good, recovering theoretical results similar to those of Kearns, Mansour and Ng [7], but with “sample complexity” bounds that have only a polynomial rather than exponential dependence on the horizon time. Our method applies to arbitrary POMDPs, including ones with infinite state and action spaces. We also present empirical results for our approach on a small discrete problem, and on a complex continuous state/continuous action problem involving learning to ride a bicycle.
Using EM for Reinforcement Learning
(1997)

We discsus Hinton’s (1989) relative payoff procedure (RPP), a static reinforcement learning algorithm whose foundation is not stochastic gradient ascent. We show circumstances under which applying the RPP is guaranteed to increase the mean return, even though it can make large changes in the values of the parameters. The proof is based on a mapping between the RPP and a form of the expectation-maximisation procedure of Dempster, Laird & Rubin (1976).
Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization
(2017)

Learning to remember long sequences remains a challenging task for recurrent neural networks. Register memory and attention mechanisms were both proposed to resolve the issue with either high computational cost to retain memory differentiability, or by discounting the RNN representation learning towards encoding shorter local contexts than encouraging long sequence encoding. Associative memory, which studies the compression of multiple patterns in a fixed size memory, were rarely considered in recent years. Although some recent work tries to introduce associative memory in RNN and mimic the energy decay process in Hopfield nets, it inherits the shortcoming of rule-based memory updates, and the memory capacity is limited. This paper proposes a method to learn the memory update rule jointly with task objective to improve memory capacity for remembering long sequences. Also, we propose an architecture that uses multiple such associative memory for more complex input encoding. We observed some interesting facts when compared to other RNN architectures on some well-studied sequence learning tasks.
Image Retargeting Quality Assessment: A Study of Subjective Scores and Objective Metrics
(2012)

This paper presents the result of a recent large-scale subjective study of image retargeting quality on a collection of images generated by several representative image retargeting methods. Owning to many approaches to image retargeting that have been developed, there is a need for a diverse independent public database of the retargeted images and the corresponding subjective scores to be freely available. We build an image retargeting quality database, in which 171 retargeted images (obtained from 57 natural source images of different contents) were created by several representative image retargeting methods. And the perceptual quality of each image is subjectively rated by at least 30 viewers, meanwhile the mean opinion scores (MOS) were obtained. It is revealed that the subject viewers have arrived at a reasonable agreement on the perceptual quality of the retargeted image. Therefore, the MOS values obtained can be regarded as the ground truth for evaluating the quality metric performances. The database is made publicly available (Image Retargeting Subjective Database, [Online]. Available: http://ivp.ee.cuhk.edu.hk/projects/demo/retargeting/index.html) to the research community in order to further research on the perceptual quality assessment of the retargeted images. Moreover, the built image retargeting database is analyzed from the perspectives of the retargeting scale, the retargeting method, and the source image content. We discuss how to retarget the images according to the scale requirement and the source image attribute information. Furthermore, several publicly available quality metrics for the retargeted images are evaluated on the built database. How to develop an effective quality metric for retargeted images is discussed through a specifically designed subjective testing process. It is demonstrated that the metric performance can be further improved, by fusing the descriptors of shape distortion and content information loss.
The ecology of human performance: a framework for considering the effect of context.
(1994)

In theory and in practice, context (as an area of concern to occupational therapists) has not received the same attention as performance components and performance areas. The Ecology of Human Performance serves as a framework for considering the effect of context. Context is described as a lens from which persons view their world. The interrelationship of person and context determines which tasks fall within the person's performance range. The Ecology of Human Performance framework provides guidelines for encompassing context in occupational therapy theory, practice, and research.
Particle swarm optimization for hyper-parameter selection in deep neural networks
(2017)

Deep neural networks (DNNs) have achieved unprecedented success in a wide array of tasks. However, the performance of these systems depends directly on their hyper-parameters which often must be selected by an expert. Optimizing the hyper-parameters remains a substantial obstacle in designing DNNs in practice. In this work, we propose to select them using particle swarm optimization (PSO). Such biologically-inspired approaches have not been extensively exploited for this task. We demonstrate that PSO efficiently explores the solution space, allowing DNNs of a minimal topology to obtain competitive classification performance over the MNIST dataset. We showed that very small DNNs optimized by PSO retrieve promising classification accuracy for CIFAR-10. Also, PSO improves the performance of existing architectures. Extensive experimental study, backed-up with the statistical tests, revealed that PSO is an effective technique for automating hyper-parameter selection and efficiently exploits computational resources.
Learning user-specific parameters in a multibiometric system
(2002)

Biometric systems that use a single biometric trait have to contend with noisy data, restricted degrees of freedom, failureto-enroll problems, spoof attacks, and unacceptable error rates. Multibiometric systems that use multiple traits of an individual for authentication, alleviate some of these problems while improving verification performance. We demonstrate that the performance of multibiometric systems can be further improved by learning user-specific parameters. Two types of parameters are considered here. (i) Thresholds that are used to decide if a matching score indicates a genuine user or an impostor, and (ii) weights that are used to indicate the importance of matching scores output by each biometric trait. User-specific thresholds are computed using the cumulative histogram of impostor matching scores corresponding to each user. The user-specific weights associated with each biometric are estimated by searching for that set of weights which minimizes the total verification error. The tests were conducted on a database of 50 users who provided fingerprint, face and hand geometry data, with 10 of these users providing data over a period of two months. We observed that user-specific thresholds improved system performance by ∼ 2%, while user-specific weights improved performance by ∼ 3%.
Effects of level of meditation experience on attentional focus: is the efficiency of executive or orientation networks improved?
(2007)

The present investigation examined the contributions of specific attentional networks to long-term trait effects of meditation. It was hypothesized that meditation could improve the efficiency of executive processing (inhibits prepotent/incorrect responses) or orientational processing (orients to specific objects in the attentional field). Participants (50 meditators and 10 controls) were given the Stroop (measures executive attention) and Global-Local Letters (measures orientational attention) tasks. Results showed that meditation experience was associated with reduced interference on the Stroop task (p < 0.03), in contrast with a lack of effect on interference in the Global-Local Letters task. This suggests that meditation produces long-term increases in the efficiency of the executive attentional network (anterior cingulate/prefrontal cortex) but no effect on the orientation network (parietal systems). The amount of time participants spent meditating each day, rather than the total number of hours of meditative practice over their lifetime, was negatively correlated with interference on the Stroop task (r = -0.31, p < 0.005).
Sarcasm detection of tweets: A comparative study
(2017)

Sarcasm is a nuanced form of communication where the individual states opposite of what is implied. One of the major challenges of sarcasm detection is its ambiguous nature. There is no prescribed definition of sarcasm. Another major challenge is the growing size of the languages. Every day hundreds of new slang words are being created and used on these sites. Hence, the existing corpus of positive and negative sentiments may not prove to be accurate in detecting sarcasm. Also, the recent developments in online social networks allow its users to use varied kind of emoticons with the text. These emoticons may change the polarity of the text and make it sarcastic. Due to these difficulties and the inherently tricky nature of sarcasm it is generally ignored during social network analysis. As a result the results of such analysis are affected adversely. Thus, sarcasm detection poses to be one of the most critical problems which we need to overcome. Detection of sarcastic content is vital to various NLP based systems such as text summarization and sentiment analysis. In this paper we address the problem of sarcasm detection by leveraging the most common expression of sarcasm — “positive sentiment attached to a negative situation”. Our work uses two ensemble based approaches — voted ensemble classifier and random forest classifier. Unlike current approaches to sarcasm detection which rely on existing corpus of positive and negative sentiments for training the classifiers, we use a seeding algorithm to generate training corpus. The proposed model also uses a pragmatic classifier to detect emoticon based sarcasm.
Components for parametric urban design in Grasshopper from street network to building geometry
(2011)

The main contribution of our work is in combining the methods for parametric urban design of highly specialized software such as CityEngine and general-purpose parametric modeling platform such as Grasshopper. Our work facilitates and prompts the use of parametric tools by architects and planners for urban design. In this paper we present a custom grasshopper component for street network generation and block subdivision. The component was developed in C# using the RhinoCommon SDK. We used Grasshopper for the development of an urban design proposal at a teaching exercise. To meet the requirements of the urban design project, additional functionalities had to be added to the range of existing Grasshopper components. In particular, we needed components for street network generation and block subdivision. To develop the component we implemented the street expansion strategies described in (Weber et al., 2009) and the methods for block subdivision described in (Vanegas et al., 2009). Additionally, we adapted and enhanced the strategies to meet the NURBS modeling capabilities of Rhinoceros.